TAILIEUCHUNG - Báo cáo khoa học: "Extraction of Entailed Semantic Relations Through Syntax-based Comma Resolution"

This paper studies textual inference by investigating comma structures, which are highly frequent elements whose major role in the extraction of semantic relations has not been hitherto recognized. We introduce the problem of comma resolution, defined as understanding the role of commas and extracting the relations they imply. We show the importance of the problem using examples from Textual Entailment tasks, and present A Sentence Transformation Rule Learner (ASTRL), a machine learning algorithm that uses a syntactic analysis of the sentence to learn sentence transformation rules that can then be used to extract relations. . | Extraction of Entailed Semantic Relations Through Syntax-based Comma Resolution Vivek Srikumar 1 Roi Reichart2 Mark Sammons1 Ari Rappoport2 Dan Roth1 University of Illinois at Urbana-Champaign vsrikum2 mssammon danr @ 2Institute of Computer Science Hebrew University of Jerusalem roiri arir @ Abstract This paper studies textual inference by investigating comma structures which are highly frequent elements whose major role in the extraction of semantic relations has not been hitherto recognized. We introduce the problem of comma resolution defined as understanding the role of commas and extracting the relations they imply. We show the importance of the problem using examples from Textual Entailment tasks and present A Sentence Transformation Rule Learner ASTRL a machine learning algorithm that uses a syntactic analysis of the sentence to learn sentence transformation rules that can then be used to extract relations. We have manually annotated a corpus identifying comma structures and relations they entail and experimented with both gold standard parses and parses created by a leading statistical parser obtaining F-scores of and respectively. 1 Introduction Recognizing relations expressed in text sentences is a major topic in NLP fundamental in applications such as Textual Entailment or Inference Question Answering and Text Mining. In this paper we address this issue from a novel perspective that of understanding the role of the commas in a sentence which we argue is a key component in sentence comprehension. Consider for example the following three sentences 1. Authorities have arrested John Smith a retired police officer. 2. Authorities have arrested John Smith his friend and his brother. 3. Authorities have arrested John Smith a retired police officer announced this morning. Sentence 1 states that John Smith is a retired police officer. The comma and surrounding sentence structure represent the relation IsA . In 2 the comma and .

• Báo cáo khoa học
• Extraction of Entailed Semantic Relations
• Syntax based Comma Resolution
• Vivek Srikumar
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học